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Yang Z, Inagaki M, Gerfen C, Fontolan L, Inagaki HK. The frontal cortex adjusts striatal integrator dynamics for flexible motor timing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.29.601348. [PMID: 39005437 PMCID: PMC11244898 DOI: 10.1101/2024.06.29.601348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Flexible control of motor timing is crucial for behavior. Before movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets. This activity may function as an adjustable 'timer,' triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviors, distinguishing their individual roles in this timer function remains challenging. To address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. Additionally, the frontal cortex, but not the striatum, encodes trial-history information guiding lick timing. These findings suggest specialized functional allocations within the forebrain: the striatum temporally integrates input from the frontal cortex to generate ramping activity that regulates motor timing.
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2
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Fenton AA. Remapping revisited: how the hippocampus represents different spaces. Nat Rev Neurosci 2024; 25:428-448. [PMID: 38714834 DOI: 10.1038/s41583-024-00817-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 05/25/2024]
Abstract
The representation of distinct spaces by hippocampal place cells has been linked to changes in their place fields (the locations in the environment where the place cells discharge strongly), a phenomenon that has been termed 'remapping'. Remapping has been assumed to be accompanied by the reorganization of subsecond cofiring relationships among the place cells, potentially maximizing hippocampal information coding capacity. However, several observations challenge this standard view. For example, place cells exhibit mixed selectivity, encode non-positional variables, can have multiple place fields and exhibit unreliable discharge in fixed environments. Furthermore, recent evidence suggests that, when measured at subsecond timescales, the moment-to-moment cofiring of a pair of cells in one environment is remarkably similar in another environment, despite remapping. Here, I propose that remapping is a misnomer for the changes in place fields across environments and suggest instead that internally organized manifold representations of hippocampal activity are actively registered to different environments to enable navigation, promote memory and organize knowledge.
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Affiliation(s)
- André A Fenton
- Center for Neural Science, New York University, New York, NY, USA.
- Neuroscience Institute at the NYU Langone Medical Center, New York, NY, USA.
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3
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Vinograd A, Nair A, Linderman SW, Anderson DJ. Intrinsic Dynamics and Neural Implementation of a Hypothalamic Line Attractor Encoding an Internal Behavioral State. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.595051. [PMID: 38826298 PMCID: PMC11142118 DOI: 10.1101/2024.05.21.595051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting. We hypothesized that these dynamics may encode continuous variation in the intensity of an internal aggressive state. Here, we report that these neurons also show line attractor dynamics in head-fixed mice observing aggression. We exploit this finding to identify and perturb line attractor-contributing neurons using 2-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations demonstrate that integration and persistent activity are intrinsic properties of these neurons which drive the system along the line attractor, while transient off-manifold perturbations reveal rapid relaxation back into the attractor. Furthermore, stimulation and imaging reveal selective functional connectivity among attractor-contributing neurons. Intriguingly, individual differences among mice in line attractor stability were correlated with the degree of functional connectivity among contributing neurons. Mechanistic modelling indicates that dense subnetwork connectivity and slow neurotransmission are required to explain our empirical findings. Our work bridges circuit and manifold paradigms, shedding light on the intrinsic and operational dynamics of a behaviorally relevant mammalian line attractor.
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Affiliation(s)
- Amit Vinograd
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
| | - Aditya Nair
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
| | - Scott W. Linderman
- Department of Statistics, Stanford University, Stanford, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, USA
| | - David J. Anderson
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
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4
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Manley J, Lu S, Barber K, Demas J, Kim H, Meyer D, Traub FM, Vaziri A. Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number. Neuron 2024; 112:1694-1709.e5. [PMID: 38452763 PMCID: PMC11098699 DOI: 10.1016/j.neuron.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 05/18/2023] [Accepted: 02/14/2024] [Indexed: 03/09/2024]
Abstract
The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.
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Affiliation(s)
- Jason Manley
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA; The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Sihao Lu
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Kevin Barber
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Jeffrey Demas
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA; The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Hyewon Kim
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - David Meyer
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Francisca Martínez Traub
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA; The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA.
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5
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Talpir I, Livneh Y. Stereotyped goal-directed manifold dynamics in the insular cortex. Cell Rep 2024; 43:114027. [PMID: 38568813 PMCID: PMC11063631 DOI: 10.1016/j.celrep.2024.114027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/12/2024] [Accepted: 03/15/2024] [Indexed: 04/05/2024] Open
Abstract
The insular cortex is involved in diverse processes, including bodily homeostasis, emotions, and cognition. However, we lack a comprehensive understanding of how it processes information at the level of neuronal populations. We leveraged recent advances in unsupervised machine learning to study insular cortex population activity patterns (i.e., neuronal manifold) in mice performing goal-directed behaviors. We find that the insular cortex activity manifold is remarkably consistent across different animals and under different motivational states. Activity dynamics within the neuronal manifold are highly stereotyped during rewarded trials, enabling robust prediction of single-trial outcomes across different mice and across various natural and artificial motivational states. Comparing goal-directed behavior with self-paced free consumption, we find that the stereotyped activity patterns reflect task-dependent goal-directed reward anticipation, and not licking, taste, or positive valence. These findings reveal a core computation in insular cortex that could explain its involvement in pathologies involving aberrant motivations.
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Affiliation(s)
- Itay Talpir
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yoav Livneh
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
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6
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Shahbazi E, Ma T, Pernuš M, Scheirer W, Afraz A. Perceptography unveils the causal contribution of inferior temporal cortex to visual perception. Nat Commun 2024; 15:3347. [PMID: 38637553 PMCID: PMC11026389 DOI: 10.1038/s41467-024-47356-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 03/28/2024] [Indexed: 04/20/2024] Open
Abstract
Neurons in the inferotemporal (IT) cortex respond selectively to complex visual features, implying their role in object perception. However, perception is subjective and cannot be read out from neural responses; thus, bridging the causal gap between neural activity and perception demands independent characterization of perception. Historically, though, the complexity of the perceptual alterations induced by artificial stimulation of IT cortex has rendered them impossible to quantify. To address this old problem, we tasked male macaque monkeys to detect and report optical impulses delivered to their IT cortex. Combining machine learning with high-throughput behavioral optogenetics, we generated complex and highly specific images that were hard for the animal to distinguish from the state of being cortically stimulated. These images, named "perceptograms" for the first time, reveal and depict the contents of the complex hallucinatory percepts induced by local neural perturbation in IT cortex. Furthermore, we found that the nature and magnitude of these hallucinations highly depend on concurrent visual input, stimulation location, and intensity. Objective characterization of stimulation-induced perceptual events opens the door to developing a mechanistic theory of visual perception. Further, it enables us to make better visual prosthetic devices and gain a greater understanding of visual hallucinations in mental disorders.
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Affiliation(s)
- Elia Shahbazi
- National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Timothy Ma
- Center for Neural Science, New York University, New York, NY, USA
| | - Martin Pernuš
- Laboratory for Machine Intelligence (LMI), University of Ljubljana, Ljubljana, Slovenia
| | - Walter Scheirer
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Arash Afraz
- National Institutes of Health (NIH), Bethesda, MD, USA
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7
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Ziemba CM, Goris RLT, Stine GM, Perez RK, Simoncelli EP, Movshon JA. Neuronal and behavioral responses to naturalistic texture images in macaque monkeys. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581645. [PMID: 38464304 PMCID: PMC10925125 DOI: 10.1101/2024.02.22.581645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and thus unlikely to reflect a critical aspect of the formation of perceptual decisions. Significance statement As visual signals propagate along the cortical hierarchy, they encode increasingly complex aspects of the sensory environment and likely have a more direct relationship with perceptual experience. We replicate and extend previous results from anesthetized monkeys differentiating the selectivity of neurons along the first step in cortical vision from area V1 to V2. However, our results further complicate efforts to establish neural signatures that reveal the relationship between perception and the neuronal activity of sensory populations. We find that choice-correlated activity in V1 and V2 is unstable across different observers and tasks, and also untethered from neuronal sensitivity and other features of nonsensory response modulation.
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8
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Pospisil DA, Aragon MJ, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Jefferis GSXE, Murthy M, Pillow JW. From connectome to effectome: learning the causal interaction map of the fly brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.31.564922. [PMID: 37961285 PMCID: PMC10635032 DOI: 10.1101/2023.10.31.564922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.
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Affiliation(s)
- Dean A Pospisil
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Max J Aragon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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9
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Gort J. Emergence of Universal Computations Through Neural Manifold Dynamics. Neural Comput 2024; 36:227-270. [PMID: 38101328 DOI: 10.1162/neco_a_01631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/05/2023] [Indexed: 12/17/2023]
Abstract
There is growing evidence that many forms of neural computation may be implemented by low-dimensional dynamics unfolding at the population scale. However, neither the connectivity structure nor the general capabilities of these embedded dynamical processes are currently understood. In this work, the two most common formalisms of firing-rate models are evaluated using tools from analysis, topology, and nonlinear dynamics in order to provide plausible explanations for these problems. It is shown that low-rank structured connectivities predict the formation of invariant and globally attracting manifolds in all these models. Regarding the dynamics arising in these manifolds, it is proved they are topologically equivalent across the considered formalisms. This letter also shows that under the low-rank hypothesis, the flows emerging in neural manifolds, including input-driven systems, are universal, which broadens previous findings. It explores how low-dimensional orbits can bear the production of continuous sets of muscular trajectories, the implementation of central pattern generators, and the storage of memory states. These dynamics can robustly simulate any Turing machine over arbitrary bounded memory strings, virtually endowing rate models with the power of universal computation. In addition, the letter shows how the low-rank hypothesis predicts the parsimonious correlation structure observed in cortical activity. Finally, it discusses how this theory could provide a useful tool from which to study neuropsychological phenomena using mathematical methods.
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Affiliation(s)
- Joan Gort
- Facultat de Psicologia, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain
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10
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Manley J, Demas J, Kim H, Traub FM, Vaziri A. Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an unbounded scaling of dimensionality with neuron number. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575721. [PMID: 38293036 PMCID: PMC10827059 DOI: 10.1101/2024.01.15.575721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The brain's remarkable properties arise from collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, what would be the biological utility of such a redundant and metabolically costly encoding scheme and what is the appropriate resolution and scale of neural recording to understand brain function? Imaging the activity of one million neurons at cellular resolution and near-simultaneously across mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number. While half of the neural variance lies within sixteen behavior-related dimensions, we find this unbounded scaling of dimensionality to correspond to an ever-increasing number of internal variables without immediate behavioral correlates. The activity patterns underlying these higher dimensions are fine-grained and cortex-wide, highlighting that large-scale recording is required to uncover the full neural substrates of internal and potentially cognitive processes.
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Affiliation(s)
- Jason Manley
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Jeffrey Demas
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Hyewon Kim
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Francisca Martínez Traub
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
- Lead Contact
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11
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Azadi R, Lopez E, Taubert J, Patterson A, Afraz A. Inactivation of face-selective neurons alters eye movements when free viewing faces. Proc Natl Acad Sci U S A 2024; 121:e2309906121. [PMID: 38198528 PMCID: PMC10801883 DOI: 10.1073/pnas.2309906121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/06/2023] [Indexed: 01/12/2024] Open
Abstract
During free viewing, faces attract gaze and induce specific fixation patterns corresponding to the facial features. This suggests that neurons encoding the facial features are in the causal chain that steers the eyes. However, there is no physiological evidence to support a mechanistic link between face-encoding neurons in high-level visual areas and the oculomotor system. In this study, we targeted the middle face patches of the inferior temporal (IT) cortex in two macaque monkeys using an functional magnetic resonance imaging (fMRI) localizer. We then utilized muscimol microinjection to unilaterally suppress IT neural activity inside and outside the face patches and recorded eye movements while the animals free viewing natural scenes. Inactivation of the face-selective neurons altered the pattern of eye movements on faces: The monkeys found faces in the scene but neglected the eye contralateral to the inactivation hemisphere. These findings reveal the causal contribution of the high-level visual cortex in eye movements.
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Affiliation(s)
- Reza Azadi
- Unit on Neurons, Circuits and Behavior, Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD20892
| | - Emily Lopez
- Unit on Neurons, Circuits and Behavior, Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD20892
| | - Jessica Taubert
- Section on Neurocircuitry, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, MD20892
- School of Psychology, The University of Queensland, Brisbane, QLD4072, Australia
| | - Amanda Patterson
- Section on Neurocircuitry, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, MD20892
| | - Arash Afraz
- Unit on Neurons, Circuits and Behavior, Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD20892
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12
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McGinty VB, Lupkin SM. Behavioral read-out from population value signals in primate orbitofrontal cortex. Nat Neurosci 2023; 26:2203-2212. [PMID: 37932464 PMCID: PMC11006434 DOI: 10.1038/s41593-023-01473-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 09/26/2023] [Indexed: 11/08/2023]
Abstract
The primate orbitofrontal cortex (OFC) has long been recognized for its role in value-based decisions; however, the exact mechanism linking value representations in the OFC to decision outcomes has remained elusive. Here, to address this question, we show, in non-human primates, that trial-wise variability in choices can be explained by variability in value signals decoded from many simultaneously recorded OFC neurons. Mechanistically, this relationship is consistent with the projection of activity within a low-dimensional value-encoding subspace onto a potentially higher-dimensional, behaviorally potent output subspace. Identifying this neural-behavioral link answers longstanding questions about the role of the OFC in economic decision-making and suggests population-level read-out mechanisms for the OFC similar to those recently identified in sensory and motor cortex.
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Affiliation(s)
- Vincent B McGinty
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ, USA.
| | - Shira M Lupkin
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ, USA
- Behavioral and Neural Sciences Graduate Program, Rutgers University-Newark, Newark, NJ, USA
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13
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Mountoufaris G, Nair A, Yang B, Kim DW, Anderson DJ. Neuropeptide Signaling is Required to Implement a Line Attractor Encoding a Persistent Internal Behavioral State. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565073. [PMID: 37961374 PMCID: PMC10635056 DOI: 10.1101/2023.11.01.565073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Internal states drive survival behaviors, but their neural implementation is not well understood. Recently we identified a line attractor in the ventromedial hypothalamus (VMH) that represents an internal state of aggressiveness. Line attractors can be implemented by recurrent connectivity and/or neuromodulatory signaling, but evidence for the latter is scant. Here we show that neuropeptidergic signaling is necessary for line attractor dynamics in this system, using a novel approach that integrates cell type-specific, anatomically restricted CRISPR/Cas9-based gene editing with microendoscopic calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1 + neurons that control aggression suppressed attack, reduced persistent neural activity and eliminated line attractor dynamics, while only modestly impacting neural activity and sex- or behavior-tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.
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14
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Jajcay N, Hlinka J. Towards a dynamical understanding of microstate analysis of M/EEG data. Neuroimage 2023; 281:120371. [PMID: 37716592 DOI: 10.1016/j.neuroimage.2023.120371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023] Open
Abstract
One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three "classically" used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.
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Affiliation(s)
- Nikola Jajcay
- Center for Advanced Studies of Brain and Consciousness, National Institute of Mental Health, Klecany, 250 67, Czech Republic; Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, 182 07, Czech Republic.
| | - Jaroslav Hlinka
- Center for Advanced Studies of Brain and Consciousness, National Institute of Mental Health, Klecany, 250 67, Czech Republic; Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, 182 07, Czech Republic.
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15
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Levy ERJ, Carrillo-Segura S, Park EH, Redman WT, Hurtado JR, Chung S, Fenton AA. A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields. Cell Rep 2023; 42:113142. [PMID: 37742193 PMCID: PMC10842170 DOI: 10.1016/j.celrep.2023.113142] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 06/14/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023] Open
Abstract
Hippocampus place cell discharge is temporally unreliable across seconds and days, and place fields are multimodal, suggesting an "ensemble cofiring" spatial coding hypothesis with manifold dynamics that does not require reliable spatial tuning, in contrast to hypotheses based on place field (spatial tuning) stability. We imaged mouse CA1 (cornu ammonis 1) ensembles in two environments across three weeks to evaluate these coding hypotheses. While place fields "remap," being more distinct between than within environments, coactivity relationships generally change less. Decoding location and environment from 1-s ensemble location-specific activity is effective and improves with experience. Decoding environment from cell-pair coactivity relationships is also effective and improves with experience, even after removing place tuning. Discriminating environments from 1-s ensemble coactivity relies crucially on the cells with the most anti-coactive cell-pair relationships because activity is internally organized on a low-dimensional manifold of non-linear coactivity relationships that intermittently reregisters to environments according to the anti-cofiring subpopulation activity.
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Affiliation(s)
| | - Simón Carrillo-Segura
- Center for Neural Science, New York University, New York, NY 10003, USA; Graduate Program in Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
| | - Eun Hye Park
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - William Thomas Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, NY 10003, USA; Flatiron Institute Center for Computational Neuroscience, New York, NY 10010, USA
| | - André Antonio Fenton
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute at the NYU Langone Medical Center, New York, NY 10016, USA.
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16
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Munn BR, Müller EJ, Medel V, Naismith SL, Lizier JT, Sanders RD, Shine JM. Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states. Nat Commun 2023; 14:6846. [PMID: 37891167 PMCID: PMC10611774 DOI: 10.1038/s41467-023-42465-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
The human brain displays a rich repertoire of states that emerge from the microscopic interactions of cortical and subcortical neurons. Difficulties inherent within large-scale simultaneous neuronal recording limit our ability to link biophysical processes at the microscale to emergent macroscopic brain states. Here we introduce a microscale biophysical network model of layer-5 pyramidal neurons that display graded coarse-sampled dynamics matching those observed in macroscale electrophysiological recordings from macaques and humans. We invert our model to identify the neuronal spike and burst dynamics that differentiate unconscious, dreaming, and awake arousal states and provide insights into their functional signatures. We further show that neuromodulatory arousal can mediate different modes of neuronal dynamics around a low-dimensional energy landscape, which in turn changes the response of the model to external stimuli. Our results highlight the promise of multiscale modelling to bridge theories of consciousness across spatiotemporal scales.
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Affiliation(s)
- Brandon R Munn
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia.
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
| | - Eli J Müller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Vicente Medel
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
| | - Sharon L Naismith
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- School of Psychology, Faculty of Science & Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Joseph T Lizier
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Robert D Sanders
- Department of Anaesthetics & Institute of Academic Surgery, Royal Prince Alfred Hospital, Camperdown, Australia
- Central Clinical School & NHMRC Clinical Trials Centre, The University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
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17
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Zeng Z, Zhang C, Gu Y. Visuo-vestibular heading perception: a model system to study multi-sensory decision making. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220334. [PMID: 37545303 PMCID: PMC10404926 DOI: 10.1098/rstb.2022.0334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/15/2023] [Indexed: 08/08/2023] Open
Abstract
Integrating noisy signals across time as well as sensory modalities, a process named multi-sensory decision making (MSDM), is an essential strategy for making more accurate and sensitive decisions in complex environments. Although this field is just emerging, recent extraordinary works from different perspectives, including computational theory, psychophysical behaviour and neurophysiology, begin to shed new light onto MSDM. In the current review, we focus on MSDM by using a model system of visuo-vestibular heading. Combining well-controlled behavioural paradigms on virtual-reality systems, single-unit recordings, causal manipulations and computational theory based on spiking activity, recent progress reveals that vestibular signals contain complex temporal dynamics in many brain regions, including unisensory, multi-sensory and sensory-motor association areas. This challenges the brain for cue integration across time and sensory modality such as optic flow which mainly contains a motion velocity signal. In addition, new evidence from the higher-level decision-related areas, mostly in the posterior and frontal/prefrontal regions, helps revise our conventional thought on how signals from different sensory modalities may be processed, converged, and moment-by-moment accumulated through neural circuits for forming a unified, optimal perceptual decision. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
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Affiliation(s)
- Zhao Zeng
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, 200031 Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049 Beijing, People's Republic of China
| | - Ce Zhang
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, 200031 Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049 Beijing, People's Republic of China
| | - Yong Gu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, 200031 Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049 Beijing, People's Republic of China
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18
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Rowland JM, van der Plas TL, Loidolt M, Lees RM, Keeling J, Dehning J, Akam T, Priesemann V, Packer AM. Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Nat Neurosci 2023; 26:1584-1594. [PMID: 37640911 PMCID: PMC10471496 DOI: 10.1038/s41593-023-01413-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions-the primary and secondary somatosensory cortex (S1 and S2)-in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.
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Affiliation(s)
- James M Rowland
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Thijs L van der Plas
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Matthias Loidolt
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Laboratory for Molecular Cell Biology, University College London, London, UK
| | - Robert M Lees
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Science and Technology Facilities Council, Octopus Imaging Facility, Research Complex at Harwell, Harwell Campus, Oxfordshire, UK
| | - Joshua Keeling
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Jonas Dehning
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Thomas Akam
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Adam M Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK.
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19
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Bounds HA, Sadahiro M, Hendricks WD, Gajowa M, Gopakumar K, Quintana D, Tasic B, Daigle TL, Zeng H, Oldenburg IA, Adesnik H. All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse. Cell Rep 2023; 42:112909. [PMID: 37542722 PMCID: PMC10755854 DOI: 10.1016/j.celrep.2023.112909] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/23/2023] [Accepted: 07/14/2023] [Indexed: 08/07/2023] Open
Abstract
Determining which features of the neural code drive behavior requires the ability to simultaneously read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two-photon calcium imaging and targeted photostimulation enable the activation of specific, functionally defined groups of neurons. However, these techniques are unable to test how patterns of activity across a population contribute to computation because of an inability to both read and write cell-specific firing rates. To overcome this challenge, we make two advances: first, we introduce a genetic line of mice for Cre-dependent co-expression of a calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we develop a method for read-out and write-in of precise population vectors of neural activity by calibrating the photostimulation to each cell. These advances offer a powerful and convenient platform for investigating the neural codes of computation and behavior.
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Affiliation(s)
- Hayley A Bounds
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA; The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Masato Sadahiro
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - William D Hendricks
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Marta Gajowa
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Karthika Gopakumar
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Quintana
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ian Antón Oldenburg
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA; The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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20
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Daie K, Fontolan L, Druckmann S, Svoboda K. Feedforward amplification in recurrent networks underlies paradoxical neural coding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552026. [PMID: 37577599 PMCID: PMC10418196 DOI: 10.1101/2023.08.04.552026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The activity of single neurons encodes behavioral variables, such as sensory stimuli (Hubel & Wiesel 1959) and behavioral choice (Britten et al. 1992; Guo et al. 2014), but their influence on behavior is often mysterious. We estimated the influence of a unit of neural activity on behavioral choice from recordings in anterior lateral motor cortex (ALM) in mice performing a memory-guided movement task (H. K. Inagaki et al. 2018). Choice selectivity grew as it flowed through a sequence of directions in activity space. Early directions carried little selectivity but were predicted to have a large behavioral influence, while late directions carried large selectivity and little behavioral influence. Consequently, estimated behavioral influence was only weakly correlated with choice selectivity; a large proportion of neurons selective for one choice were predicted to influence choice in the opposite direction. These results were consistent with models in which recurrent circuits produce feedforward amplification (Goldman 2009; Ganguli et al. 2008; Murphy & Miller 2009) so that small amplitude signals along early directions are amplified to produce low-dimensional choice selectivity along the late directions, and behavior. Targeted photostimulation experiments (Daie et al. 2021b) revealed that activity along the early directions triggered sequential activity along the later directions and caused predictable behavioral biases. These results demonstrate the existence of an amplifying feedforward dynamical motif in the motor cortex, explain paradoxical responses to perturbation experiments (Chettih & Harvey 2019; Daie et al. 2021b; Russell et al. 2019), and reveal behavioral relevance of small amplitude neural dynamics.
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21
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Afraz A. Behavioral optogenetics in nonhuman primates; a psychological perspective. CURRENT RESEARCH IN NEUROBIOLOGY 2023; 5:100101. [PMID: 38020813 PMCID: PMC10663131 DOI: 10.1016/j.crneur.2023.100101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 06/02/2023] [Accepted: 06/22/2023] [Indexed: 12/01/2023] Open
Abstract
Optogenetics has been a promising and developing technology in systems neuroscience throughout the past decade. It has been difficult though to reliably establish the potential behavioral effects of optogenetic perturbation of the neural activity in nonhuman primates. This poses a challenge on the future of optogenetics in humans as the concepts and technology need to be developed in nonhuman primates first. Here, I briefly summarize the viable approaches taken to improve nonhuman primate behavioral optogenetics, then focus on one approach: improvements in the measurement of behavior. I bring examples from visual behavior and show how the choice of method of measurement might conceal large behavioral effects. I will then discuss the "cortical perturbation detection" task in detail as an example of a sensitive task that can record the behavioral effects of optogenetic cortical stimulation with high fidelity. Finally, encouraged by the rich scientific landscape ahead of behavioral optogenetics, I invite technology developers to improve the chronically implantable devices designed for simultaneous neural recording and optogenetic intervention in nonhuman primates.
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Affiliation(s)
- Arash Afraz
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institute of Health, Bethesda, Maryland, USA
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22
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Akitake B, Douglas HM, LaFosse PK, Beiran M, Deveau CE, O'Rawe J, Li AJ, Ryan LN, Duffy SP, Zhou Z, Deng Y, Rajan K, Histed MH. Amplified cortical neural responses as animals learn to use novel activity patterns. Curr Biol 2023; 33:2163-2174.e4. [PMID: 37148876 DOI: 10.1016/j.cub.2023.04.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 02/09/2023] [Accepted: 04/14/2023] [Indexed: 05/08/2023]
Abstract
Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting that the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found that their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task; therefore, these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.
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Affiliation(s)
- Bradley Akitake
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hannah M Douglas
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Paul K LaFosse
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Manuel Beiran
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ciana E Deveau
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jonathan O'Rawe
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Anna J Li
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lauren N Ryan
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Samuel P Duffy
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhishang Zhou
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yanting Deng
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kanaka Rajan
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mark H Histed
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA.
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23
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Naik S, Dehaene-Lambertz G, Battaglia D. Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:4847. [PMID: 37430760 DOI: 10.3390/s23104847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 07/12/2023]
Abstract
Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention of sufficient trials become crucial. Here, we present one such algorithm that makes use of large spatiotemporal correlations in neural signals and solves the low-rank matrix completion problem, to fix artifactual entries. The method uses a gradient descent algorithm in lower dimensions to learn the missing entries and provide faithful reconstruction of signals. We carried out numerical simulations to benchmark the method and estimate optimal hyperparameters for actual EEG data. The fidelity of reconstruction was assessed by detecting event-related potentials (ERP) from a highly artifacted EEG time series from human infants. The proposed method significantly improved the standardized error of the mean in ERP group analysis and a between-trial variability analysis compared to a state-of-the-art interpolation technique. This improvement increased the statistical power and revealed significant effects that would have been deemed insignificant without reconstruction. The method can be applied to any time-continuous neural signal where artifacts are sparse and spread out across epochs and channels, increasing data retention and statistical power.
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Affiliation(s)
- Shruti Naik
- Cognitive Neuroimaging Unit, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), CEA, Université Paris-Saclay, NeuroSpin Center, F-91190 Gif-sur-Yvette, France
| | - Ghislaine Dehaene-Lambertz
- Cognitive Neuroimaging Unit, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), CEA, Université Paris-Saclay, NeuroSpin Center, F-91190 Gif-sur-Yvette, France
| | - Demian Battaglia
- Institut de Neurosciences des Systèmes, U1106, Centre National de la Recherche Scientifique (CNRS) Aix-Marseille Université, F-13005 Marseille, France
- Institute for Advanced Studies, University of Strasbourg, (USIAS), F-67000 Strasbourg, France
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24
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Watkins de Jong L, Nejad MM, Yoon E, Cheng S, Diba K. Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3. Curr Biol 2023; 33:1689-1703.e5. [PMID: 37023753 PMCID: PMC10175182 DOI: 10.1016/j.cub.2023.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 02/22/2023] [Accepted: 03/10/2023] [Indexed: 04/08/2023]
Abstract
Recurrent connectivity between excitatory neurons and the strength of feedback from inhibitory neurons are critical determinants of the dynamics and computational properties of neuronal circuits. Toward a better understanding of these circuit properties in regions CA1 and CA3 of the hippocampus, we performed optogenetic manipulations combined with large-scale unit recordings in rats under anesthesia and in quiet waking, using photoinhibition and photoexcitation with different light-sensitive opsins. In both regions, we saw striking paradoxical responses: subsets of cells increased firing during photoinhibition, while other cells decreased firing during photoexcitation. These paradoxical responses were more prominent in CA3 than in CA1, but, notably, CA1 interneurons showed increased firing in response to photoinhibition of CA3. These observations were recapitulated in simulations where we modeled both CA1 and CA3 as inhibition-stabilized networks in which strong recurrent excitation is balanced by feedback inhibition. To directly test the inhibition-stabilized model, we performed large-scale photoinhibition directed at (GAD-Cre) inhibitory cells and found that interneurons in both regions increased firing when photoinhibited, as predicted. Our results highlight the often-paradoxical circuit dynamics that are evidenced during optogenetic manipulations and indicate that, contrary to long-standing dogma, both CA1 and CA3 hippocampal regions display strongly recurrent excitation, which is stabilized through inhibition.
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Affiliation(s)
- Laurel Watkins de Jong
- Department of Anesthesiology, Michigan Medicine, 1150 W. Medical Center Dr, Ann Arbor, MI 48109, USA; Department of Psychology, University of Wisconsin-Milwaukee, 2441 E Hartford Ave, Milwaukee, WI 53211, USA
| | | | - Euisik Yoon
- Department of Electrical Engineering and Computer Science, 1301 Beal Avenue, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sen Cheng
- Institute for Neural Computation, Ruhr University Bochum, Universitätsstr. 150, 44801 Bochum, Germany
| | - Kamran Diba
- Department of Anesthesiology, Michigan Medicine, 1150 W. Medical Center Dr, Ann Arbor, MI 48109, USA; Department of Psychology, University of Wisconsin-Milwaukee, 2441 E Hartford Ave, Milwaukee, WI 53211, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA.
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25
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Pancholi R, Sun-Yan A, Peron S. Microstimulation of sensory cortex engages natural sensory representations. Curr Biol 2023; 33:1765-1777.e5. [PMID: 37130521 PMCID: PMC10246453 DOI: 10.1016/j.cub.2023.03.085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 05/04/2023]
Abstract
Cortical activity patterns occupy a small subset of possible network states. If this is due to intrinsic network properties, microstimulation of sensory cortex should evoke activity patterns resembling those observed during natural sensory input. Here, we use optical microstimulation of virally transfected layer 2/3 pyramidal neurons in the mouse primary vibrissal somatosensory cortex to compare artificially evoked activity with natural activity evoked by whisker touch and movement ("whisking"). We find that photostimulation engages touch- but not whisking-responsive neurons more than expected by chance. Neurons that respond to photostimulation and touch or to touch alone exhibit higher spontaneous pairwise correlations than purely photoresponsive neurons. Exposure to several days of simultaneous touch and optogenetic stimulation raises both overlap and spontaneous activity correlations among touch and photoresponsive neurons. We thus find that cortical microstimulation engages existing cortical representations and that repeated co-presentation of natural and artificial stimulation enhances this effect.
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Affiliation(s)
- Ravi Pancholi
- Center for Neural Science, New York University, 4 Washington Pl., Rm. 621, New York, NY 10003, USA
| | - Andrew Sun-Yan
- Center for Neural Science, New York University, 4 Washington Pl., Rm. 621, New York, NY 10003, USA
| | - Simon Peron
- Center for Neural Science, New York University, 4 Washington Pl., Rm. 621, New York, NY 10003, USA.
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26
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Dygalo NN. Connectivity of the Brain in the Light of Chemogenetic Modulation of Neuronal Activity. Acta Naturae 2023; 15:4-13. [PMID: 37538804 PMCID: PMC10395778 DOI: 10.32607/actanaturae.11895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/10/2023] [Indexed: 08/05/2023] Open
Abstract
Connectivity is the coordinated activity of the neuronal networks responsible for brain functions; it is detected based on functional magnetic resonance imaging signals that depend on the oxygen level in the blood (blood oxygen level-dependent (BOLD) signals) supplying the brain. The BOLD signal is only indirectly related to the underlying neuronal activity; therefore, it remains an open question whether connectivity and changes in it are only manifestations of normal and pathological states of the brain or they are, to some extent, the causes of these states. The creation of chemogenetic receptors activated by synthetic drugs (designer receptors exclusively activated by designer drugs, DREADDs), which, depending on the receptor type, either facilitate or, on the contrary, inhibit the neuronal response to received physiological stimuli, makes it possible to assess brain connectivity in the light of controlled neuronal activity. Evidence suggests that connectivity is based on neuronal activity and is a manifestation of connections between brain regions that integrate sensory, cognitive, and motor functions. Chemogenetic modulation of the activity of various groups and types of neurons changes the connectivity of the brain and its complex functions. Chemogenetics can be useful in reconfiguring the pathological mechanisms of nervous and mental diseases. The initiated integration, based on the whole-brain connectome from molecular-cellular, neuronal, and synaptic processes to higher nervous activity and behavior, has the potential to significantly increase the fundamental and applied value of this branch of neuroscience.
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Affiliation(s)
- N. N. Dygalo
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (IC&G SB RAS), Novosibirsk, 630090 Russian Federation
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Chauhan T, Jakovljev I, Thompson LN, Wuerger SM, Martinovic J. Decoding of EEG signals reveals non-uniformities in the neural geometry of colour. Neuroimage 2023; 268:119884. [PMID: 36657691 DOI: 10.1016/j.neuroimage.2023.119884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 11/04/2022] [Accepted: 01/15/2023] [Indexed: 01/19/2023] Open
Abstract
The idea of colour opponency maintains that colour vision arises through the comparison of two chromatic mechanisms, red versus green and yellow versus blue. The four unique hues, red, green, blue, and yellow, are assumed to appear at the null points of these the two chromatic systems. Here we hypothesise that, if unique hues represent a tractable cortical state, they should elicit more robust activity compared to other, non-unique hues. We use a spatiotemporal decoding approach to report that electroencephalographic (EEG) responses carry robust information about the tested isoluminant unique hues within a 100-350 ms window from stimulus onset. Decoding is possible in both passive and active viewing tasks, but is compromised when concurrent high luminance contrast is added to the colour signals. For large hue-differences, the efficiency of hue decoding can be predicted by mutual distance in a nominally uniform perceptual colour space. However, for small perceptual neighbourhoods around unique hues, the encoding space shows pivotal non-uniformities which suggest that anisotropies in neurometric hue-spaces may reflect perceptual unique hues.
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Affiliation(s)
- Tushar Chauhan
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 02139 Cambridge MA, USA.
| | - Ivana Jakovljev
- Department of Psychology. Faculty of Philosophy, University of Novi Sad, Serbia
| | | | - Sophie M Wuerger
- Department of Psychology, University of Liverpool, Liverpool, L697ZA, UK
| | - Jasna Martinovic
- School of Psychology, University of Aberdeen, Aberdeen, AB24 3FX, UK; Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, EH8 9JZ, UK.
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28
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Prilutski Y, Livneh Y. Physiological Needs: Sensations and Predictions in the Insular Cortex. Physiology (Bethesda) 2023; 38:0. [PMID: 36040864 DOI: 10.1152/physiol.00019.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Physiological needs create powerful motivations (e.g., thirst and hunger). Studies in humans and animal models have implicated the insular cortex in the neural regulation of physiological needs and need-driven behavior. We review prominent mechanistic models of how the insular cortex might achieve this regulation and present a conceptual and analytical framework for testing these models in healthy and pathological conditions.
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Affiliation(s)
- Yael Prilutski
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Yoav Livneh
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
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Azadi R, Bohn S, Lopez E, Lafer-Sousa R, Wang K, Eldridge MAG, Afraz A. Image-dependence of the detectability of optogenetic stimulation in macaque inferotemporal cortex. Curr Biol 2023; 33:581-588.e4. [PMID: 36610394 PMCID: PMC9905296 DOI: 10.1016/j.cub.2022.12.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/24/2022] [Accepted: 12/08/2022] [Indexed: 01/09/2023]
Abstract
Artificial activation of neurons in early visual areas induces perception of simple visual flashes.1,2 Accordingly, stimulation in high-level visual cortices is expected to induce perception of complex features.3,4 However, results from studies in human patients challenge this expectation. Stimulation rarely induces any detectable visual event, and never a complex one, in human subjects with closed eyes.2 Stimulation of the face-selective cortex in a human patient led to remarkable hallucinations only while the subject was looking at faces.5 In contrast, stimulations of color- and face-selective sites evoke notable hallucinations independent of the object being viewed.6 These anecdotal observations suggest that stimulation of high-level visual cortex can evoke perception of complex visual features, but these effects depend on the availability and content of visual input. In this study, we introduce a novel psychophysical task to systematically investigate characteristics of the perceptual events evoked by optogenetic stimulation of macaque inferior temporal (IT) cortex. We trained macaque monkeys to detect and report optogenetic impulses delivered to their IT cortices7,8,9 while holding fixation on object images. In a series of experiments, we show that detection of cortical stimulation is highly dependent on the choice of images presented to the eyes and it is most difficult when fixating on a blank screen. These findings suggest that optogenetic stimulation of high-level visual cortex results in easily detectable distortions of the concurrent contents of vision.
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Affiliation(s)
- Reza Azadi
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA.
| | - Simon Bohn
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emily Lopez
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Rosa Lafer-Sousa
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Karen Wang
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Mark A G Eldridge
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
| | - Arash Afraz
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA
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Reversible Inactivation of Ferret Auditory Cortex Impairs Spatial and Nonspatial Hearing. J Neurosci 2023; 43:749-763. [PMID: 36604168 PMCID: PMC9899081 DOI: 10.1523/jneurosci.1426-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/16/2022] [Accepted: 11/29/2022] [Indexed: 01/06/2023] Open
Abstract
A key question in auditory neuroscience is to what extent are brain regions functionally specialized for processing specific sound features, such as location and identity. In auditory cortex, correlations between neural activity and sounds support both the specialization of distinct cortical subfields, and encoding of multiple sound features within individual cortical areas. However, few studies have tested the contribution of auditory cortex to hearing in multiple contexts. Here we determined the role of ferret primary auditory cortex in both spatial and nonspatial hearing by reversibly inactivating the middle ectosylvian gyrus during behavior using cooling (n = 2 females) or optogenetics (n = 1 female). Optogenetic experiments used the mDLx promoter to express Channelrhodopsin-2 in GABAergic interneurons, and we confirmed both viral expression (n = 2 females) and light-driven suppression of spiking activity in auditory cortex, recorded using Neuropixels under anesthesia (n = 465 units from 2 additional untrained female ferrets). Cortical inactivation via cooling or optogenetics impaired vowel discrimination in colocated noise. Ferrets implanted with cooling loops were tested in additional conditions that revealed no deficit when identifying vowels in clean conditions, or when the temporally coincident vowel and noise were spatially separated by 180 degrees. These animals did, however, show impaired sound localization when inactivating the same auditory cortical region implicated in vowel discrimination in noise. Our results demonstrate that, as a brain region showing mixed selectivity for spatial and nonspatial features of sound, primary auditory cortex contributes to multiple forms of hearing.SIGNIFICANCE STATEMENT Neurons in primary auditory cortex are often sensitive to the location and identity of sounds. Here we inactivated auditory cortex during spatial and nonspatial listening tasks using cooling, or optogenetics. Auditory cortical inactivation impaired multiple behaviors, demonstrating a role in both the analysis of sound location and identity and confirming a functional contribution of mixed selectivity observed in neural activity. Parallel optogenetic experiments in two additional untrained ferrets linked behavior to physiology by demonstrating that expression of Channelrhodopsin-2 permitted rapid light-driven suppression of auditory cortical activity recorded under anesthesia.
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31
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Galgali AR, Sahani M, Mante V. Residual dynamics resolves recurrent contributions to neural computation. Nat Neurosci 2023; 26:326-338. [PMID: 36635498 DOI: 10.1038/s41593-022-01230-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 11/08/2022] [Indexed: 01/14/2023]
Abstract
Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals-that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.
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Affiliation(s)
- Aniruddh R Galgali
- Institute of Neuroinformatics, University of Zurich & ETH Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich & ETH Zurich, Zurich, Switzerland.
- Department of Experimental Psychology, University of Oxford, Oxford, UK.
| | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Valerio Mante
- Institute of Neuroinformatics, University of Zurich & ETH Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich & ETH Zurich, Zurich, Switzerland.
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32
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van der Plas TL, Tubiana J, Le Goc G, Migault G, Kunst M, Baier H, Bormuth V, Englitz B, Debrégeas G. Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity. eLife 2023; 12:83139. [PMID: 36648065 PMCID: PMC9940913 DOI: 10.7554/elife.83139] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/15/2023] [Indexed: 01/18/2023] Open
Abstract
Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet, it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here, we recorded the activity from ∼40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ∼200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.
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Affiliation(s)
- Thijs L van der Plas
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Center for Neuroscience, Radboud UniversityNijmegenNetherlands
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
- Department of Physiology, Anatomy and Genetics, University of OxfordOxfordUnited Kingdom
| | - Jérôme Tubiana
- Blavatnik School of Computer Science, Tel Aviv UniversityTel AvivIsrael
| | - Guillaume Le Goc
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
| | - Geoffrey Migault
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
| | - Michael Kunst
- Department Genes – Circuits – Behavior, Max Planck Institute for Biological IntelligenceMartinsriedGermany
- Allen Institute for Brain ScienceSeattleUnited States
| | - Herwig Baier
- Department Genes – Circuits – Behavior, Max Planck Institute for Biological IntelligenceMartinsriedGermany
| | - Volker Bormuth
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
| | - Bernhard Englitz
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Center for Neuroscience, Radboud UniversityNijmegenNetherlands
| | - Georges Debrégeas
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP)ParisFrance
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33
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Kob L. Exploring the role of structuralist methodology in the neuroscience of consciousness: a defense and analysis. Neurosci Conscious 2023; 2023:niad011. [PMID: 37205986 PMCID: PMC10191193 DOI: 10.1093/nc/niad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/27/2023] [Accepted: 04/13/2023] [Indexed: 05/21/2023] Open
Abstract
Traditional contrastive analysis has been the foundation of consciousness science, but its limitations due to the lack of a reliable method for measuring states of consciousness have prompted the exploration of alternative approaches. Structuralist theories have gained attention as an alternative that focuses on the structural properties of phenomenal experience and seeks to identify their neural encoding via structural similarities between quality spaces and neural state spaces. However, the intertwining of philosophical assumptions about structuralism and structuralist methodology may pose a challenge to those who are skeptical of the former. In this paper, I offer an analysis and defense of structuralism as a methodological approach in consciousness science, which is partly independent of structuralist assumptions on the nature of consciousness. By doing so, I aim to make structuralist methodology more accessible to a broader scientific and philosophical audience. I situate methodological structuralism in the context of questions concerning mental representation, psychophysical measurement, holism, and functional relevance of neural processes. At last, I analyze the relationship between the structural approach and the distinction between conscious and unconscious states.
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Affiliation(s)
- Lukas Kob
- *Corresponding author. Philosophy Department, Otto-von-Guericke University, Zschokkestraße 32, Magdeburg 39104, Germany. E-mail:
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34
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Barbosa J, Stein H, Zorowitz S, Niv Y, Summerfield C, Soto-Faraco S, Hyafil A. A practical guide for studying human behavior in the lab. Behav Res Methods 2023; 55:58-76. [PMID: 35262897 DOI: 10.3758/s13428-022-01793-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 11/08/2022]
Abstract
In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.
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Affiliation(s)
- Joao Barbosa
- Brain Circuits & Behavior lab, IDIBAPS, Barcelona, Spain.
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Supérieure - PSL Research University, 75005, Paris, France.
| | - Heike Stein
- Brain Circuits & Behavior lab, IDIBAPS, Barcelona, Spain
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Supérieure - PSL Research University, 75005, Paris, France
| | - Sam Zorowitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Yael Niv
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Psychology, Princeton University, Princeton, USA
| | | | - Salvador Soto-Faraco
- Multisensory Research Group, Center for Brain and Cognition, Universitat Pompeu Fabra Barcelona, Spain, and Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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35
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Lafer-Sousa R, Wang K, Azadi R, Lopez E, Bohn S, Afraz A. Behavioral detectability of optogenetic stimulation of inferior temporal cortex varies with the size of concurrently viewed objects. CURRENT RESEARCH IN NEUROBIOLOGY 2022; 4:100063. [PMID: 36578652 PMCID: PMC9791129 DOI: 10.1016/j.crneur.2022.100063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/21/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
We have previously demonstrated that macaque monkeys can behaviorally detect a subtle optogenetic impulse delivered to their inferior temporal (IT) cortex. We have also shown that the ability to detect the cortical stimulation impulse varies depending on some characteristics of the visual images viewed at the time of brain stimulation, revealing the visual nature of the perceptual events induced by stimulation of the IT cortex. Here we systematically studied the effect of the size of viewed objects on behavioral detectability of optogenetic stimulation of the central IT cortex. Surprisingly, we found that behavioral detection of the same optogenetic impulse highly varies with the size of the viewed object images. Reduction of the object size in four steps from 8 to 1 degree of visual angle significantly decreased detection performance. These results show that identical stimulation impulses delivered to the same neural population induce variable perceptual events depending on the mere size of the objects viewed at the time of brain stimulation.
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Affiliation(s)
- Rosa Lafer-Sousa
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA,Corresponding author.
| | - Karen Wang
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Reza Azadi
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Emily Lopez
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Simon Bohn
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA,Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Arash Afraz
- Laboratory of Neuropsychology, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
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36
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Christensen AJ, Ott T, Kepecs A. Cognition and the single neuron: How cell types construct the dynamic computations of frontal cortex. Curr Opin Neurobiol 2022; 77:102630. [PMID: 36209695 PMCID: PMC10375540 DOI: 10.1016/j.conb.2022.102630] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/18/2022] [Accepted: 08/23/2022] [Indexed: 01/10/2023]
Abstract
Frontal cortex is thought to underlie many advanced cognitive capacities, from self-control to long term planning. Reflecting these diverse demands, frontal neural activity is notoriously idiosyncratic, with tuning properties that are correlated with endless numbers of behavioral and task features. This menagerie of tuning has made it difficult to extract organizing principles that govern frontal neural activity. Here, we contrast two successful yet seemingly incompatible approaches that have begun to address this challenge. Inspired by the indecipherability of single-neuron tuning, the first approach casts frontal computations as dynamical trajectories traversed by arbitrary mixtures of neurons. The second approach, by contrast, attempts to explain the functional diversity of frontal activity with the biological diversity of cortical cell-types. Motivated by the recent discovery of functional clusters in frontal neurons, we propose a consilience between these population and cell-type-specific approaches to neural computations, advancing the conjecture that evolutionarily inherited cell-type constraints create the scaffold within which frontal population dynamics must operate.
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Affiliation(s)
- Amelia J Christensen
- Department of Neuroscience and Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, USA.
| | - Torben Ott
- Department of Neuroscience and Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, USA; Bernstein Center for Computational Neuroscience Berlin, Humboldt University of Berlin, Berlin, Germany.
| | - Adam Kepecs
- Department of Neuroscience and Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, USA.
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37
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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38
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Barack DL, Miller EK, Moore CI, Packer AM, Pessoa L, Ross LN, Rust NC. A call for more clarity around causality in neuroscience. Trends Neurosci 2022; 45:654-655. [PMID: 35810023 PMCID: PMC9996677 DOI: 10.1016/j.tins.2022.06.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 06/10/2022] [Indexed: 11/29/2022]
Abstract
In neuroscience, the term 'causality' is used to refer to different concepts, leading to confusion. Here we illustrate some of those variations, and we suggest names for them. We then introduce four ways to enhance clarity around causality in neuroscience.
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Affiliation(s)
- David L Barack
- Departments of Neuroscience and Philosophy, University of Pennsylvania, Philadelphia, PA, USA.
| | - Earl K Miller
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Christopher I Moore
- Carney Institute for Brain Science, Department of Neuroscience, Brown University, Providence, RI, USA.
| | - Adam M Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK.
| | - Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
| | - Lauren N Ross
- Department of Logic and Philosophy of Science, University of California, Irvine, CA, USA.
| | - Nicole C Rust
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
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39
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Flavell SW, Gogolla N, Lovett-Barron M, Zelikowsky M. The emergence and influence of internal states. Neuron 2022; 110:2545-2570. [PMID: 35643077 PMCID: PMC9391310 DOI: 10.1016/j.neuron.2022.04.030] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 02/11/2022] [Accepted: 04/27/2022] [Indexed: 01/09/2023]
Abstract
Animal behavior is shaped by a variety of "internal states"-partially hidden variables that profoundly shape perception, cognition, and action. The neural basis of internal states, such as fear, arousal, hunger, motivation, aggression, and many others, is a prominent focus of research efforts across animal phyla. Internal states can be inferred from changes in behavior, physiology, and neural dynamics and are characterized by properties such as pleiotropy, persistence, scalability, generalizability, and valence. To date, it remains unclear how internal states and their properties are generated by nervous systems. Here, we review recent progress, which has been driven by advances in behavioral quantification, cellular manipulations, and neural population recordings. We synthesize research implicating defined subsets of state-inducing cell types, widespread changes in neural activity, and neuromodulation in the formation and updating of internal states. In addition to highlighting the significance of these findings, our review advocates for new approaches to clarify the underpinnings of internal brain states across the animal kingdom.
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Affiliation(s)
- Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Nadine Gogolla
- Emotion Research Department, Max Planck Institute of Psychiatry, 80804 Munich, Germany; Circuits for Emotion Research Group, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany.
| | - Matthew Lovett-Barron
- Division of Biological Sciences-Neurobiology Section, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Moriel Zelikowsky
- Department of Neurobiology, University of Utah, Salt Lake City, UT 84112, USA.
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40
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Yang B, Karigo T, Anderson DJ. Transformations of neural representations in a social behaviour network. Nature 2022; 608:741-749. [PMID: 35922505 PMCID: PMC9529293 DOI: 10.1038/s41586-022-05057-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/29/2022] [Indexed: 02/05/2023]
Abstract
Mating and aggression are innate social behaviours that are controlled by subcortical circuits in the extended amygdala and hypothalamus1-4. The bed nucleus of the stria terminalis (BNSTpr) is a node that receives input encoding sex-specific olfactory cues from the medial amygdala5,6, and which in turn projects to hypothalamic nuclei that control mating7-9 (medial preoptic area (MPOA)) and aggression9-14 (ventromedial hypothalamus, ventrolateral subdivision (VMHvl)), respectively15. Previous studies have demonstrated that male aromatase-positive BNSTpr neurons are required for mounting and attack, and may identify conspecific sex according to their overall level of activity16. However, neural representations in BNSTpr, their function and their transformations in the hypothalamus have not been characterized. Here we performed calcium imaging17,18 of male BNSTprEsr1 neurons during social behaviours. We identify distinct populations of female- versus male-tuned neurons in BNSTpr, with the former outnumbering the latter by around two to one, similar to the medial amygdala and MPOA but opposite to VMHvl, in which male-tuned neurons predominate6,9,19. Chemogenetic silencing of BNSTprEsr1 neurons while imaging MPOAEsr1 or VMHvlEsr1 neurons in behaving animals showed, unexpectedly, that the male-dominant sex-tuning bias in VMHvl was inverted to female-dominant whereas a switch from sniff- to mount-selective neurons during mating was attenuated in MPOA. Our data also indicate that BNSTprEsr1 neurons are not essential for conspecific sex identification. Rather, they control the transition from appetitive to consummatory phases of male social behaviours by shaping sex- and behaviour-specific neural representations in the hypothalamus.
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Affiliation(s)
- Bin Yang
- Division of Biology and Biological Engineering 140-80, TianQiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena CA 91125 USA,Howard Hughes Medical Institute, California Institute of Technology, 1200 E California Blvd, Pasadena CA 91125 USA
| | - Tomomi Karigo
- Division of Biology and Biological Engineering 140-80, TianQiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena CA 91125 USA,Howard Hughes Medical Institute, California Institute of Technology, 1200 E California Blvd, Pasadena CA 91125 USA
| | - David J. Anderson
- Division of Biology and Biological Engineering 140-80, TianQiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena CA 91125 USA,Howard Hughes Medical Institute, California Institute of Technology, 1200 E California Blvd, Pasadena CA 91125 USA, Author for correspondence: Tel: (626) 395-6821, FAX: (626) 564-8243,
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41
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Capouskova K, Kringelbach ML, Deco G. Modes of cognition: Evidence from metastable brain dynamics. Neuroimage 2022; 260:119489. [PMID: 35882268 DOI: 10.1016/j.neuroimage.2022.119489] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 01/31/2023] Open
Abstract
Managing cognitive load depends on adequate resource allocation by the human brain through the engagement of metastable substates, which are large-scale functional networks that change over time. We employed a novel analysis method, deep autoencoder dynamical analysis (DADA), with 100 healthy adults selected from the Human Connectome Project (HCP) data set in rest and six cognitive tasks. The deep autoencoder of DADA described seven recurrent stochastic metastable substates from the functional connectome of BOLD phase coherence matrices. These substates were significantly differentiated in terms of their probability of appearance, time duration, and spatial attributes. We found that during different cognitive tasks, there was a higher probability of having more connected substates dominated by a high degree of connectivity in the thalamus. In addition, compared with those during tasks, resting brain dynamics have a lower level of predictability, indicating a more uniform distribution of metastability between substates, quantified by higher entropy. These novel findings provide empirical evidence for the philosophically motivated cognitive theory, suggesting on-line and off-line as two fundamentally distinct modes of cognition. On-line cognition refers to task-dependent engagement with the sensory input, while off-line cognition is a slower, environmentally detached mode engaged with decision and planning. Overall, the DADA framework provides a bridge between neuroscience and cognitive theory that can be further explored in the future.
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Affiliation(s)
- Katerina Capouskova
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain.
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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42
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Inagaki HK, Chen S, Daie K, Finkelstein A, Fontolan L, Romani S, Svoboda K. Neural Algorithms and Circuits for Motor Planning. Annu Rev Neurosci 2022; 45:249-271. [PMID: 35316610 DOI: 10.1146/annurev-neuro-092021-121730] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.
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Affiliation(s)
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Kayvon Daie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
| | - Arseny Finkelstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Lorenzo Fontolan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
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43
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Wöstmann M, Störmer VS, Obleser J, Addleman DA, Andersen SK, Gaspelin N, Geng JJ, Luck SJ, Noonan MP, Slagter HA, Theeuwes J. Ten simple rules to study distractor suppression. Prog Neurobiol 2022. [PMID: 35427732 DOI: 10.1016/j.pneurobio.2022.102269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Distractor suppression refers to the ability to filter out distracting and task-irrelevant information. Distractor suppression is essential for survival and considered a key aspect of selective attention. Despite the recent and rapidly evolving literature on distractor suppression, we still know little about how the brain suppresses distracting information. What limits progress is that we lack mutually agreed upon principles of how to study the neural basis of distractor suppression and its manifestation in behavior. Here, we offer ten simple rules that we believe are fundamental when investigating distractor suppression. We provide guidelines on how to design conclusive experiments on distractor suppression (Rules 1-3), discuss different types of distractor suppression that need to be distinguished (Rules 4-6), and provide an overview of models of distractor suppression and considerations of how to evaluate distractor suppression statistically (Rules 7-10). Together, these rules provide a concise and comprehensive synopsis of promising advances in the field of distractor suppression. Following these rules will propel research on distractor suppression in important ways, not only by highlighting prominent issues to both new and more advanced researchers in the field, but also by facilitating communication between sub-disciplines.
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Affiliation(s)
- Malte Wöstmann
- Department of Psychology, University of Lübeck, Lübeck, Germany; Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany.
| | - Viola S Störmer
- Department of Psychological and Brain Sciences, Dartmouth College, USA.
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Lübeck, Germany; Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | | | - Søren K Andersen
- School of Psychology, University of Aberdeen, UK; Department of Psychology, University of Southern Denmark, Denmark
| | - Nicholas Gaspelin
- Department of Psychology and Department of Integrative Neuroscience, Binghamton University, State University of New York, USA
| | - Joy J Geng
- Center for Mind and Brain and Department of Psychology, University of California, Davis, USA
| | - Steven J Luck
- Center for Mind and Brain and Department of Psychology, University of California, Davis, USA
| | | | - Heleen A Slagter
- Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Institute for Brain and Behavior, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jan Theeuwes
- Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Institute for Brain and Behavior, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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44
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Masset P, Qin S, Zavatone-Veth JA. Drifting neuronal representations: Bug or feature? BIOLOGICAL CYBERNETICS 2022; 116:253-266. [PMID: 34993613 DOI: 10.1007/s00422-021-00916-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/17/2021] [Indexed: 06/14/2023]
Abstract
The brain displays a remarkable ability to sustain stable memories, allowing animals to execute precise behaviors or recall stimulus associations years after they were first learned. Yet, recent long-term recording experiments have revealed that single-neuron representations continuously change over time, contravening the classical assumption that learned features remain static. How do unstable neural codes support robust perception, memories, and actions? Here, we review recent experimental evidence for such representational drift across brain areas, as well as dissections of its functional characteristics and underlying mechanisms. We emphasize theoretical proposals for how drift need not only be a form of noise for which the brain must compensate. Rather, it can emerge from computationally beneficial mechanisms in hierarchical networks performing robust probabilistic computations.
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Affiliation(s)
- Paul Masset
- Center for Brain Science, Harvard University, Cambridge, MA, USA.
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| | - Shanshan Qin
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Jacob A Zavatone-Veth
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
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45
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Wheatcroft T, Saleem AB, Solomon SG. Functional Organisation of the Mouse Superior Colliculus. Front Neural Circuits 2022; 16:792959. [PMID: 35601532 PMCID: PMC9118347 DOI: 10.3389/fncir.2022.792959] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/07/2022] [Indexed: 11/30/2022] Open
Abstract
The superior colliculus (SC) is a highly conserved area of the mammalian midbrain that is widely implicated in the organisation and control of behaviour. SC receives input from a large number of brain areas, and provides outputs to a large number of areas. The convergence and divergence of anatomical connections with different areas and systems provides challenges for understanding how SC contributes to behaviour. Recent work in mouse has provided large anatomical datasets, and a wealth of new data from experiments that identify and manipulate different cells within SC, and their inputs and outputs, during simple behaviours. These data offer an opportunity to better understand the roles that SC plays in these behaviours. However, some of the observations appear, at first sight, to be contradictory. Here we review this recent work and hypothesise a simple framework which can capture the observations, that requires only a small change to previous models. Specifically, the functional organisation of SC can be explained by supposing that three largely distinct circuits support three largely distinct classes of simple behaviours-arrest, turning towards, and the triggering of escape or capture. These behaviours are hypothesised to be supported by the optic, intermediate and deep layers, respectively.
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Affiliation(s)
| | | | - Samuel G. Solomon
- Institute of Behavioural Neuroscience, University College London, London, United Kingdom
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46
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47
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Hacker CM, Rust NC. Ritalin as a causal perturbation. Trends Cogn Sci 2022; 26:542-543. [DOI: 10.1016/j.tics.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/15/2022] [Indexed: 10/18/2022]
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48
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Wöstmann M, Störmer VS, Obleser J, Addleman DA, Andersen SK, Gaspelin N, Geng JJ, Luck SJ, Noonan MP, Slagter HA, Theeuwes J. Ten simple rules to study distractor suppression. Prog Neurobiol 2022; 213:102269. [PMID: 35427732 PMCID: PMC9069241 DOI: 10.1016/j.pneurobio.2022.102269] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 01/23/2023]
Abstract
Distractor suppression refers to the ability to filter out distracting and task-irrelevant information. Distractor suppression is essential for survival and considered a key aspect of selective attention. Despite the recent and rapidly evolving literature on distractor suppression, we still know little about how the brain suppresses distracting information. What limits progress is that we lack mutually agreed upon principles of how to study the neural basis of distractor suppression and its manifestation in behavior. Here, we offer ten simple rules that we believe are fundamental when investigating distractor suppression. We provide guidelines on how to design conclusive experiments on distractor suppression (Rules 1–3), discuss different types of distractor suppression that need to be distinguished (Rules 4–6), and provide an overview of models of distractor suppression and considerations of how to evaluate distractor suppression statistically (Rules 7–10). Together, these rules provide a concise and comprehensive synopsis of promising advances in the field of distractor suppression. Following these rules will propel research on distractor suppression in important ways, not only by highlighting prominent issues to both new and more advanced researchers in the field, but also by facilitating communication between sub-disciplines. Distractor suppression is the ability to filter out irrelevant information. At present, we know little about how the brain suppresses distraction. We offer ten rules that are fundamental when investigating distractor suppression. Following the rules will propel research and foster interaction between disciplines.
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Affiliation(s)
- Malte Wöstmann
- Department of Psychology, University of Lübeck, Lübeck, Germany; Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany.
| | - Viola S Störmer
- Department of Psychological and Brain Sciences, Dartmouth College, USA.
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Lübeck, Germany; Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | | | - Søren K Andersen
- School of Psychology, University of Aberdeen, UK; Department of Psychology, University of Southern Denmark, Denmark
| | - Nicholas Gaspelin
- Department of Psychology and Department of Integrative Neuroscience, Binghamton University, State University of New York, USA
| | - Joy J Geng
- Center for Mind and Brain and Department of Psychology, University of California, Davis, USA
| | - Steven J Luck
- Center for Mind and Brain and Department of Psychology, University of California, Davis, USA
| | | | - Heleen A Slagter
- Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Institute for Brain and Behavior, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jan Theeuwes
- Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Institute for Brain and Behavior, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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49
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Mishra P, Narayanan R. Conjunctive changes in multiple ion channels mediate activity-dependent intrinsic plasticity in hippocampal granule cells. iScience 2022; 25:103922. [PMID: 35252816 PMCID: PMC8894279 DOI: 10.1016/j.isci.2022.103922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 01/19/2022] [Accepted: 02/10/2022] [Indexed: 02/05/2023] Open
Abstract
Plasticity in the brain is ubiquitous. How do neurons and networks encode new information and simultaneously maintain homeostasis in the face of such ubiquitous plasticity? Here, we unveil a form of neuronal plasticity in rat hippocampal granule cells, which is mediated by conjunctive changes in HCN, inward-rectifier potassium, and persistent sodium channels induced by theta-modulated burst firing, a behaviorally relevant activity pattern. Cooperation and competition among these simultaneous changes resulted in a unique physiological signature: sub-threshold excitability and temporal summation were reduced without significant changes in action potential firing, together indicating a concurrent enhancement of supra-threshold excitability. This form of intrinsic plasticity was dependent on calcium influx through L-type calcium channels and inositol trisphosphate receptors. These observations demonstrate that although brain plasticity is ubiquitous, strong systemic constraints govern simultaneous plasticity in multiple components-referred here as plasticity manifolds-thereby providing a cellular substrate for concomitant encoding and homeostasis in engram cells.
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Affiliation(s)
- Poonam Mishra
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India
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50
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Wang T, Chen Y, Cui H. From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control. Neurosci Bull 2022; 38:796-808. [PMID: 35298779 PMCID: PMC9276910 DOI: 10.1007/s12264-022-00832-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/29/2021] [Indexed: 11/01/2022] Open
Abstract
In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.
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Affiliation(s)
- Tianwei Wang
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yun Chen
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - He Cui
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China. .,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
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